1. Chapter 7: Project Quality Management
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2. Quality of Information Technology Projects
Many people joke about the poor quality of IT products
People seem to accept systems being down occasionally or needing to reboot their PCs
There are many examples in the news about quality problems related to IT (See What Went Wrong?)
But quality is very important in many IT projects
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3. What Is Project Quality Management?
The International Organization for Standardization (ISO) defines quality as the totality of characteristics of an entity that bear on its ability to satisfy stated or implied needs
Other experts define quality based on
conformance to requirements: meeting written specifications
fitness for use: ensuring a product can be used as it was intended
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4. Project Quality Management Processes
Quality planning: identifying which quality standards are relevant to the project and how to satisfy them
Quality assurance: evaluating overall project performance to ensure the project will satisfy the relevant quality standards
Quality control: monitoring specific project results to ensure that they comply with the relevant quality standards while identifying ways to improve overall quality
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5. Modern Quality Management
requires customer satisfaction
prefers prevention to inspection
recognizes management responsibility for quality
Noteworthy quality experts include Deming, Juran, Crosby, Ishikawa, Taguchi, and Feigenbaum
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Quality Experts
Deming was famous for his work in rebuilding Japan and his 14 points
Juran wrote the Quality Control Handbook and 10 steps to quality improvement
Crosby wrote Quality is Free and suggested that organizations strive for zero defects
Ishikawa developed the concept of quality circles and using fishbone diagrams
Taguchi developed methods for optimizing the process of engineering experimentation
Feigenbaum developed the concept of total quality control
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7. Figure 7-1. Sample Fishbone or Ishikawa Diagram
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8. Malcolm Baldrige Award and ISO 9000
The Malcolm Baldrige Quality Award was started in 1987 to recognize companies with world-class quality
ISO 9000 provides minimum requirements for an organization to meet their quality certification standards
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Quality Planning
It is important to design in quality and communicate important factors that directly contribute to meeting the customer’s requirements
Design of experiments helps identify which variable have the most influence on the overall outcome of a process
Many scope aspects of IT projects affect quality like functionality, features, system outputs, performance, reliability, and maintainability
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Quality Assurance
Quality assurance includes all the activities related to satisfying the relevant quality standards for a project
Another goal of quality assurance is continuous quality improvement
Benchmarking can be used to generate ideas for quality improvements
Quality audits help identify lessons learned that can improve performance on current or future projects
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Quality Control
The main outputs of quality control are
acceptance decisions
rework
process adjustments
Some tools and techniques include
pareto analysis
statistical sampling
quality control charts
testing
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Pareto Analysis
Pareto analysis involves identifying the vital few contributors that account for the most quality problems in a system
Also called the rule, meaning that 80% of problems are often due to 20% of the causes
Pareto diagrams are histograms that help identify and prioritize problem areas
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13. Figure 7-2. Sample Pareto Diagram
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14. Statistical Sampling and Standard Deviation
Statistical sampling involves choosing part of a population of interest for inspection
The size of a sample depends on how representative you want the sample to be
Sample size formula:
Sample size = .25 X (certainty Factor/acceptable error)2
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15. Table 7-1. Commonly Used Certainty Factors
95% certainty: Sample size = 0.25 X (1.960/.05) 2 = 384
90% certainty: Sample size = 0.25 X (1.645/.10)2 = 68
80% certainty: Sample size = 0.25 X (1.281/.20)2 = 10
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Standard Deviation
Standard deviation measures how much variation exists in a distribution of data
A small standard deviation means that data cluster closely around the middle of a distribution and there is little variability among the data
A normal distribution is a bell-shaped curve that is symmetrical about the mean or average value of a population
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17. Figure 7-3. Normal Distribution and Standard Deviation
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18. Table 7-2. Sigma and Defective Units
Note: “Six sigma” often refers to +/-3 sigma, meaning
2.7 million defects per billion units produced, or 2.7
defects per million.
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19. Quality Control Charts, Six Sigma, and the Seven Run Rule
A control chart is a graphic display of data that illustrates the results of a process over time. It helps prevent defects and allows you to determine whether a process is in control or out of control
Operating at a higher sigma value, like 6 sigma, means the product tolerance or control limits have less variability
The seven run rule states that if seven data points in a row are all below the mean, above,the mean, or increasing or decreasing, then the process needs to be examined for non-random problems
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20. Figure 7-4. Sample Quality Control Chart
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21. Figure 7-5. Reducing Defects with Six Sigma
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Testing
Many IT professionals think of testing as a stage that comes near the end of IT product development
Testing should be done during almost every phase of the IT product development life cycle
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23. Figure 7-6. Testing Tasks in the Software Development Life Cycle
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Types of Tests
A unit test is done to test each individual component (often a program) to ensure it is as defect free as possible
Integration testing occurs between unit and system testing to test functionally grouped components
System testing tests the entire system as one entity
User acceptance testing is an independent test performed by the end user prior to accepting the delivered system
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Figure 7-7. Gantt Chart for Building Testing into a Systems Development Project Plan
Project 98 file
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26. Improving Information Technology Project Quality
Several suggestions for improving quality for IT projects include
Leadership that promotes quality
Understanding the cost of quality
Focusing on organizational influences and workplace factors that affect quality
Following maturity models to improve quality
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Leadership
“It is most important that top management be quality-minded. In the absence of sincere manifestation of interest at the top, little will happen below.” (Juran, 1945)
A large percentage of quality problems are associated with management, not technical issues
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The Cost of Quality
The cost of quality is
the cost of conformance or delivering products that meet requirements and fitness for use
the cost of nonconformance or taking responsibility for failures or not meeting quality expectations
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29. Table 7-3. Costs Per Hour of Downtime Caused by Software Defects
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30. Five Cost Categories Related to Quality
Prevention cost: the cost of planning and executing a project so it is error-free or within an acceptable error range
Appraisal cost: the cost of evaluating processes and their outputs to ensure quality
Internal failure cost: cost incurred to correct an identified defect before the customer receives the product
External failure cost: cost that relates to all errors not detected and corrected before delivery to the customer
Measurement and test equipment costs: capital cost of equipment used to perform prevention and appraisal activities
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31. Organization Influences, Workplace Factors, and Quality
Study by DeMarco and Lister showed that organizational issues had a much greater influence on programmer productivity than the technical environment or programming languages
Programmer productivity varied by a factor of one to ten across organizations, but only by 21% within the same organization
Study found no correlation between productivity and programming language, years of experience, or salary
A dedicated workspace and a quiet work environment were key factors to improving programmer productivity
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Maturity Models
Maturity models are frameworks for helping organization improve their processes and systems
Software Quality Function Deployment Model focuses on defining user requirements and planning software projects
The Software Engineering Institute’s Capability Maturity Model provides a generic path to process improvement for software development
Several groups are working on project management maturity models
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33. Project Management Maturity Model
1. Ad-Hoc: The project management process is described as disorganized, and occasionally even chaotic. The organization has not defined systems and processes, and project success depends on individual effort. There are chronic cost and schedule problems.
2. Abbreviated: There are some project management processes and systems in place to track cost, schedule, and scope. Project success is largely unpredictable and cost and schedule problems are common.
3. Organized: There are standardized, documented project management processes and systems that are integrated into the rest of the organization. Project success is more predictable, and cost and schedule performance is improved.
4. Managed: Management collects and uses detailed measures of the effectiveness of project management. Project success is more uniform, and cost and schedule performance conforms to plan.
5. Adaptive: Feedback from the project management process and from piloting innovative ideas and technologies enables continuous improvement. Project success is the norm, and cost and schedule performance is continuously improving.
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